title: Network biomarkers reveal dysfunctional gene regulations during disease progression.
abstract: Extensive studies have been conducted on gene biomarkers by exploring the increasingly accumulated gene expression and sequence data generated from high-throughput technology. Here, we briefly report on the state-of-the-art research and application of biomarkers from single genes (i.e. gene biomarkers) to gene sets (i.e. group or set biomarkers), gene networks (i.e. network biomarkers) and dynamical gene networks (i.e. dynamical network biomarkers). In particular, differential and dynamical network biomarkers are used as representative examples to demonstrate their effectiveness in both detecting early signals for complex diseases and revealing essential mechanisms on disease initiation and progression at a network level.
authors.affiliation: Key Laboratory of Systems Biology, SIBS-Novo Nordisk Translational Research Centre for PreDiabetes, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.
authors.forename: Tao
authors.initials: T
authors.lastname: Zeng
authors.forename: Shao-Yan
authors.initials: SY
authors.lastname: Sun
authors.forename: Yong
authors.initials: Y
authors.lastname: Wang
authors.forename: Hailong
authors.initials: H
authors.lastname: Zhu
authors.forename: Luonan
authors.initials: L
authors.lastname: Chen
mesh_terms: D015415:Biomarkers / Q000378:metabolism*; D003922:Diabetes Mellitus, Type 1 / Q000209:etiology / Q000235:genetics / Q000378:metabolism; D018450:Disease Progression; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D005819:Genetic Markers; D006801:Humans; D008957:Models, Genetic; D049490:Systems Biology
chemical_list: D015415:Biomarkers; D005819:Genetic Markers
keywords: differential expression network; disease diagnosis; disease prognosis; disease progression; dynamical network biomarkers; gene regulation; module biomarkers; network biomarkers; progressive module network model; systems biology